


'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase__          = logging.getLogger(__name__)


UpperCamelCase__          = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
UpperCamelCase__          = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class      lowerCamelCase_  :
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={
              'help': (
                  'The model checkpoint for weights initialization. Leave None if you want to train a model from'
                  ' scratch.'
              )
          }	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__a )}	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,			)




@dataclass
class      lowerCamelCase_  :
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'The input training data file (a text file).'} )
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={
              'help': (
                  'The input training data files (multiple files in glob format). '
                  'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
              )
          }	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}	,			)

      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
      lowerCAmelCase__							=	field(default=__a	,			metadata={'help': 'Whether ot not to use whole word mask.'} )
      lowerCAmelCase__							=	field(
          default=0.15	,			metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
      lowerCAmelCase__							=	field(
          default=1 / 6	,			metadata={
              'help': (
                  'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
                  ' modeling.'
              )
          }	,			)
      lowerCAmelCase__							=	field(
          default=5	,			metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )

      lowerCAmelCase__							=	field(
          default=-1	,			metadata={
              'help': (
                  'Optional input sequence length after tokenization.'
                  'The training dataset will be truncated in block of this size for training.'
                  'Default to the model max input length for single sentence inputs (take into account special tokens).'
              )
          }	,			)
      lowerCAmelCase__							=	field(
          default=__a	,			metadata={'help': 'Overwrite the cached training and evaluation sets'} )





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__ = False    ,   lowerCAmelCase__ = None    ,   )       ->      Tuple:
    def _dataset(lowerCAmelCase__    ,   lowerCAmelCase__=None		):
        if args.line_by_line:
            if ref_path is not None:
                if not args.whole_word_mask or not args.mlm:
                    raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''		)
                return LineByLineWithRefDataset(
                    tokenizer=lowerCAmelCase__    ,   file_path=lowerCAmelCase__    ,   block_size=args.block_size    ,   ref_path=lowerCAmelCase__    ,   )

            return LineByLineTextDataset(tokenizer=lowerCAmelCase__    ,   file_path=lowerCAmelCase__    ,   block_size=args.block_size		)
        else:
            return TextDataset(
                tokenizer=lowerCAmelCase__    ,   file_path=lowerCAmelCase__    ,   block_size=args.block_size    ,   overwrite_cache=args.overwrite_cache    ,   cache_dir=lowerCAmelCase__    ,   )

    if evaluate:
        return _dataset(args.eval_data_file    ,   args.eval_ref_file		)
    elif args.train_data_files:
        return ConcatDataset([_dataset(lowerCAmelCase__		) for f in glob(args.train_data_files		)]		)
    else:
        return _dataset(args.train_data_file    ,   args.train_ref_file		)





def    a__			(   )       ->      Optional[int]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    UpperCAmelCase__     :						Dict						=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
    UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						int						=							parser.parse_args_into_dataclasses()

    if data_args.eval_data_file is None and training_args.do_eval:
        raise ValueError(
            '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
            '''or remove the --do_eval argument.'''		)
    if (
        os.path.exists(training_args.output_dir		)
        and os.listdir(training_args.output_dir		)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
            ''' --overwrite_output_dir to overcome.'''		)

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,   datefmt='''%m/%d/%Y %H:%M:%S'''    ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,   )
    logger.warning(
        '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''    ,   training_args.local_rank    ,   training_args.device    ,   training_args.n_gpu    ,   bool(training_args.local_rank != -1		)    ,   training_args.fpaa    ,   )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank		):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('''Training/evaluation parameters %s'''    ,   lowerCAmelCase__		)

    # Set seed
    set_seed(training_args.seed		)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.config_name:
        UpperCAmelCase__     :						Any						=							AutoConfig.from_pretrained(model_args.config_name    ,   cache_dir=model_args.cache_dir		)
    elif model_args.model_name_or_path:
        UpperCAmelCase__     :						Optional[Any]						=							AutoConfig.from_pretrained(model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir		)
    else:
        UpperCAmelCase__     :						List[Any]						=							CONFIG_MAPPING[model_args.model_type]()
        logger.warning('''You are instantiating a new config instance from scratch.'''		)

    if model_args.tokenizer_name:
        UpperCAmelCase__     :						str						=							AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,   cache_dir=model_args.cache_dir		)
    elif model_args.model_name_or_path:
        UpperCAmelCase__     :						Optional[int]						=							AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir		)
    else:
        raise ValueError(
            '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
            ''' script, save it,and load it from here, using --tokenizer_name'''		)

    if model_args.model_name_or_path:
        UpperCAmelCase__     :						Dict						=							AutoModelWithLMHead.from_pretrained(
            model_args.model_name_or_path    ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)    ,   config=lowerCAmelCase__    ,   cache_dir=model_args.cache_dir    ,   )
    else:
        logger.info('''Training new model from scratch'''		)
        UpperCAmelCase__     :						List[Any]						=							AutoModelWithLMHead.from_config(lowerCAmelCase__		)

    model.resize_token_embeddings(len(lowerCAmelCase__		)		)

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
        raise ValueError(
            '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
            '''--mlm flag (masked language modeling).'''		)

    if data_args.block_size <= 0:
        UpperCAmelCase__     :						str						=							tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
        UpperCAmelCase__     :						Union[str, Any]						=							min(data_args.block_size    ,   tokenizer.max_len		)

    # Get datasets

    UpperCAmelCase__     :						Union[str, Any]						=							(
        get_dataset(lowerCAmelCase__    ,   tokenizer=lowerCAmelCase__    ,   cache_dir=model_args.cache_dir		) if training_args.do_train else None
    )
    UpperCAmelCase__     :						List[Any]						=							(
        get_dataset(lowerCAmelCase__    ,   tokenizer=lowerCAmelCase__    ,   evaluate=lowerCAmelCase__    ,   cache_dir=model_args.cache_dir		)
        if training_args.do_eval
        else None
    )
    if config.model_type == "xlnet":
        UpperCAmelCase__     :						str						=							DataCollatorForPermutationLanguageModeling(
            tokenizer=lowerCAmelCase__    ,   plm_probability=data_args.plm_probability    ,   max_span_length=data_args.max_span_length    ,   )
    else:
        if data_args.mlm and data_args.whole_word_mask:
            UpperCAmelCase__     :						Optional[int]						=							DataCollatorForWholeWordMask(
                tokenizer=lowerCAmelCase__    ,   mlm_probability=data_args.mlm_probability		)
        else:
            UpperCAmelCase__     :						Optional[int]						=							DataCollatorForLanguageModeling(
                tokenizer=lowerCAmelCase__    ,   mlm=data_args.mlm    ,   mlm_probability=data_args.mlm_probability		)

    # Initialize our Trainer
    UpperCAmelCase__     :						Union[str, Any]						=							Trainer(
        model=lowerCAmelCase__    ,   args=lowerCAmelCase__    ,   data_collator=lowerCAmelCase__    ,   train_dataset=lowerCAmelCase__    ,   eval_dataset=lowerCAmelCase__    ,   prediction_loss_only=lowerCAmelCase__    ,   )

    # Training
    if training_args.do_train:
        UpperCAmelCase__     :						Dict						=							(
            model_args.model_name_or_path
            if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		)
            else None
        )
        trainer.train(model_path=lowerCAmelCase__		)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
    UpperCAmelCase__     :						Optional[Any]						=							{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''		)

        UpperCAmelCase__     :						Tuple						=							trainer.evaluate()

        UpperCAmelCase__     :						Any						=							math.exp(eval_output['''eval_loss''']		)
        UpperCAmelCase__     :						List[str]						=							{'''perplexity''': perplexity}

        UpperCAmelCase__     :						List[str]						=							os.path.join(training_args.output_dir    ,   '''eval_results_lm.txt'''		)
        if trainer.is_world_master():
            with open(lowerCAmelCase__    ,   '''w'''		) as writer:
                logger.info('''***** Eval results *****'''		)
                for key in sorted(result.keys()		):
                    logger.info('''  %s = %s'''    ,   lowerCAmelCase__    ,   str(result[key]		)		)
                    writer.write('''%s = %s\n''' % (key, str(result[key]		))		)

        results.update(lowerCAmelCase__		)

    return results





def    a__			(   lowerCAmelCase__		)       ->      str:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()








"""simple docstring"""


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters() ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is False
																		), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is True
																		), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__=True ):





						'''simple docstring'''






						model.train()
						lowerCAmelCase   :Dict						=			model(a__ )
						lowerCAmelCase   :Union[str, Any]						=			F.mse_loss(a__     ,      target.to(output.device ) )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(a__ )


def        UpperCAmelCase      (					a__     ,      a__=False ):





						'''simple docstring'''






						set_seed(42 )
						lowerCAmelCase   :Any						=			RegressionModel()
						lowerCAmelCase   :List[str]						=			deepcopy(a__ )
						lowerCAmelCase   :str						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						model.to(accelerator.device )
						if sched:
												lowerCAmelCase   :List[Any]						=			AdamW(params=model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Dict						=			AdamW(params=ddp_model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Tuple						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
												lowerCAmelCase   :Optional[Any]						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
						# Make a copy of `model`
						if sched:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :int						=			accelerator.prepare(a__     ,      a__     ,      a__     ,      a__ )
						else:
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			accelerator.prepare(a__     ,      a__ )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :Any						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad     ,      ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Any						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Dict						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :str						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :int						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :str						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			get_training_setup(a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Dict						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Any						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Optional[int]						=			ddp_input[torch.randperm(len(a__ ) )]
						GradientState._reset_state()


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			get_training_setup(a__     ,      a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
												lowerCAmelCase   :int						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
												if accelerator.num_processes > 1:
																		check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
						GradientState._reset_state()


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Accelerator()

						lowerCAmelCase   :int						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase   :Any						=			RegressionDataset(length=96 )
						lowerCAmelCase   :Dict						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase	, lowerCAmelCase   :Tuple						=			accelerator.prepare(a__     ,      a__ )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(a__ ):
												assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
												if iteration < len(a__ ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(a__ ):
																														assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
																														if batch_num < len(a__ ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			Accelerator()
						lowerCAmelCase   :Optional[int]						=			accelerator.state
						if state.local_process_index == 0:
												print('**Test `accumulate` gradient accumulation with dataloader break**' )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print('**Test NOOP `no_sync` context manager**' )
												test_noop_sync(a__ )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print('**Test Distributed `no_sync` context manager**' )
												test_distributed_sync(a__ )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    '**Test `accumulate` gradient accumulation, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																								test_gradient_accumulation(a__     ,      a__ )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version('<'     ,      '2.0' ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      '`split_batches=False`, `dispatch_batches=False`**'     ,      )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																														test_gradient_accumulation_with_opt_and_scheduler(a__     ,      a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()